Alon Brutzkus

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 126 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #3,505 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Optimization & Training — 27 citations into it; #423 of 2,601 by trust here, #599 by citations
• Scaling & Learning Theory — 19 citations into it
• Statistical Learning Theory — 16 citations into it; #760 of 1,038 by trust here, #417 by citations
• Computational Mechanics — 12 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Model Architectures — 9 citations into it; #349 of 2,278 by trust here, #915 by citations

Whom Alon Brutzkus trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Behnam Neyshabur
6. Kaiming He
7. Peter L. Bartlett
8. Karen Simonyan
9. Ilya Sutskever
10. Yann LeCun

Who trusts Alon Brutzkus overall

1. Simon Shaolei Du
2. Zeyuan Allen-Zhu
3. Yuanzhi Li
4. Jason D. Lee
5. Samet Oymak
6. M. Soltanolkotabi
7. Prateek Jain
8. Difan Zou
9. Lili Su
10. Wei Hu

Papers from Alon Brutzkus's perspective

Ordered by how strongly the researchers Alon Brutzkus trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data (2017) — 287 citations
• Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs (2017) — 319 citations
• Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem (2018) — 79 citations
• Low Latency Privacy Preserving Inference (2018) — 296 citations